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Oracle 1z0-1157-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: Introduction to AI Agents	- AI agent fundamentals and architecture 1. AI agents, traditional chatbots, and rule-based systems 2. Agent reasoning patterns including Chain-of-Thought and ReAct 3. Core agent components: LLMs, tools, and orchestration loops 4. Safety, guardrails, and responsible agentic workflows - Agent development concepts 1. Function calling and tool use 2. Multi-agent design patterns and handoffs 3. OpenAI Agents SDK guardrails
Topic 2: Introduction to MCP	- Model Context Protocol fundamentals 1. Tool discovery and interoperability 2. MCP clients and servers 3. MCP concepts and architecture
Topic 3: Agentic AI for Oracle AI Database	- Oracle AI Database agentic AI capabilities 1. Select AI 2. Oracle Autonomous AI Database MCP Server 3. Document chunking, embedding generation, and retrieval 4. Grounding agent responses with enterprise data 5. Oracle AI Vector Search 6. Vector data types, embeddings, and similarity search 7. Oracle AI Database Private Agent Factory
Topic 4: OpenAI Responses API and Agents SDK	- OpenAI agent development 1. OpenAI Agents SDK 2. Multi-agent handoffs 3. Guardrails and tracing 4. Function calling and tools 5. Responses API
Topic 5: LangChain for AI Agents	- LangChain fundamentals 1. Building agents with LangChain 2. Agent invocation and orchestration flow 3. Tools, tool schemas, and tool execution 4. LangChain and LangChain Expression Language
Topic 6: OCI Enterprise AI Agents	- OCI Enterprise AI platform 1. OCI Enterprise AI Agents service 2. Agent development, orchestration, and execution 3. Building and running AI agents 4. Deployment and scaling 5. Responses API, tools, memory, and vector stores

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최신 Oracle Certification 1z0-1157-26 무료샘플문제 (Q27-Q32):

질문 # 27
From the LLM's perspective, what is consistent between MCP-served tools and locally defined tools?

A. The LLM receives network paths and authentication credentials for MCP tools.
B. MCP-served tools always return richer outputs than local tools.
C. The LLM can only invoke MCP-served tools after explicit user approval.
D. The LLM interacts with both through the same tool-calling interface.

정답：D

설명：
MCP standardizes how external systems expose capabilities to an AI application, but the model does not need to reason about the transport or deployment location of each capability. Once an MCP server's tools are discovered and incorporated into an agent's available tool set, they are represented to the model as callable tools with names, descriptions, and input schemas. Locally implemented function tools are presented through essentially the same model-facing tool abstraction. OpenAI's Agents SDK documentation explicitly states that tools obtained from configured MCP servers are added to the agent's list of available tools, alongside ordinary tools. Therefore, from the LLM's perspective, both are selected and invoked through the tool-calling mechanism rather than through separate network-specific interfaces.
Authentication, network connectivity, server lifecycle, authorization, and actual execution remain responsibilities of the application/MCP infrastructure. They are deliberately abstracted away from the model.
Therefore, option B precisely captures the architectural consistency described in the course question.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - MCP tools, tool discovery, agent tool abstraction, and client-server integration.

질문 # 28
Which behavior is NOT a characteristic of modern LLM-based AI agents?

A. Pursuing goals across multiple steps
B. Adjusting behavior based on observations
C. Using external APIs or databases
D. Requiring every execution path to be predefined

정답：D

설명：
Modern LLM-based agents are specifically designed to avoid requiring every possible execution path to be predetermined. The uploaded course material therefore correctly identifies "Requiring every execution path to be predefined" as the behavior that is NOT characteristic of an agent.
OpenAI defines agents as systems capable of independently accomplishing workflows using an LLM to manage workflow execution and make decisions. An agent can determine when a workflow is complete, correct its actions after receiving observations, and dynamically select tools according to the current state.
This differs fundamentally from conventional deterministic automation in which developers encode every branch and execution path beforehand.
Agents commonly pursue objectives across multiple reasoning-and-action cycles. They can invoke external APIs, databases, search systems, or other tools; inspect the resulting observations; and choose subsequent actions. A typical agent loop continues until an exit condition is reached rather than following one permanently fixed sequence.
Predetermined rules may still be used for safety, permissions, and guardrails, but the complete path toward the goal does not need to be pre-scripted.
Therefore, D is the correct answer.
Study Guide reference/topic: Introduction to AI Agents - autonomy, agent loops, observations, dynamic tool use, multi-step goal execution, and deterministic workflows.

질문 # 29
What does the @function_tool decorator do in the OpenAI Agents SDK?

A. Registers the function as an HTTP endpoint the agent calls via REST.
B. Caches the function's return value to disk for faster subsequent calls.
C. Converts a regular Python function into a tool the agent can call.
D. Automatically retries the function if it raises an exception during execution.

정답：C

설명：
The @function_tool decorator converts an ordinary Python function into a FunctionTool that can be exposed to an agent for model-directed invocation. The Agents SDK automatically derives important tool metadata: by default, the Python function name becomes the tool name, its docstring supplies the tool description, and the function signature is converted into a JSON schema describing the expected arguments. This structured representation allows the language model to determine when the function is relevant and generate valid arguments for it. The decorator does not inherently expose the function as a REST endpoint, persist return values to disk, or guarantee automatic retries whenever execution fails. This mechanism corresponds closely to OCI Enterprise AI Agents' Function Calling model, where application-controlled functions extend an agent beyond pure model generation and enable controlled interaction with external business logic. OpenAI GitHub

질문 # 30
What is the architectural advantage of Autonomous AI Database MCP Server over a separately deployed third- party MCP server?

A. It moves all query execution to a separate compute tier outside the database.
B. It eliminates the need for SQL queries entirely.
C. It uses a common MCP host for all database connections.
D. It reduces operational overhead and enforces security inside the database.

정답：D

질문 # 31
Which approaches are supported by OCI Enterprise AI Agents?

A. Call the Responses API directly or deploy a hosted agent application.
B. Build a frontend agent or a backend agent.
C. Select a free or paid deployment model.
D. Deploy using Python-only or Java-only options.

정답：A

설명：
OCI Generative AI defines two principal approaches for developing enterprise-grade agentic applications.
The first is to build agents using the OCI Responses API , an API-first model that allows developers to control agent interactions through an OpenAI-compatible interface. The second is to deploy hosted agentic applications using OCI Generative AI Applications and Deployments, where OCI manages substantial portions of the application runtime infrastructure. Oracle explicitly documents these as the two main Enterprise AI Agent approaches and notes that they can also be combined in hybrid architectures.
The Responses API approach is appropriate when developers want direct programmatic control over models, tools, context, and agent behavior without independently managing inference infrastructure. Hosted agent applications are appropriate when custom agent runtimes need managed container deployment, networking, identity, storage integration, scaling, and production lifecycle support. OCI's broader Generative AI architecture positions these mechanisms within its Enterprise AI Agents layer.
The supported architecture is therefore not divided according to Python versus Java, pricing categories, or frontend versus backend classification. Option A reproduces Oracle's documented deployment choices precisely and matches the supplied examination source.
Study Guide reference/topic: OCI Enterprise AI Agents - OCI Responses API, Applications, Deployments, hosted agentic applications, and hybrid architectures.

질문 # 32
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